{ "cells": [ { "cell_type": "markdown", "id": "ff595b47-d637-4cd4-a75e-ead6f4b84dce", "metadata": {}, "source": [ "To start, we import the `matplotlib` library, which is essential for visualizing different aspects of our calculations, such as molecular geometry, as well as the resulting IR and Raman spectra. By setting `plt.rcParams['figure.dpi'] = 120`, we adjust the resolution of the figures to ensure they are displayed with sufficient clarity and detail." ] }, { "cell_type": "code", "execution_count": 1, "id": "63c23cca-ad08-4546-8a83-c4b0465077c7", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.dpi'] = 120" ] }, { "cell_type": "markdown", "id": "fe5ed9e4-b774-4d12-996a-28d5418085bc", "metadata": {}, "source": [ "As previously mentioned, we use the PLAMS Python library to interact with AMS. The code below constructs the beryllium oxide (BeO) crystal structure by defining a `Molecule` object. Individual atoms, beryllium (Be) and oxygen (O), are then added to the molecule using their respective symbols and Cartesian coordinates, which are specified in Angstroms. Next, the crystal lattice is defined as a 3x3 matrix, with dimensions also in Angstroms." ] }, { "cell_type": "code", "execution_count": 2, "id": "4da77384-f36b-445a-9785-a863e2ff292c", "metadata": {}, "outputs": [], "source": [ "from scm.plams import Molecule, Atom\n", "\n", "mol = Molecule()\n", "mol.add_atom(Atom(symbol=\"Be\", coords=(0.00000000, 1.55554138, 0.00070090)))\n", "mol.add_atom(Atom(symbol=\"Be\", coords=(1.34713836, 0.77777069, 2.18871035)))\n", "mol.add_atom(Atom(symbol= \"O\", coords=(0.00000000, 1.55554138, 1.65124622)))\n", "mol.add_atom(Atom(symbol= \"O\", coords=(1.34713836, 0.77777069, 3.83925567)))\n", "mol.lattice = [[ 2.69427671, 0.00000000, 0.00000000], \\\n", " [-1.34713836, 2.33331208, 0.00000000], \\\n", " [ 0.00000000, 0.00000000, 4.37601889]]" ] }, { "cell_type": "markdown", "id": "e2ffbbfa-aa00-40d6-9770-258ea4964969", "metadata": {}, "source": [ "To visualize the BeO crystal structure we've defined, we employ the `plot_molecule` function. This function serves as an interface to the `plot_atoms` function from the Atomic Simulation Environment (ASE), enabling us to generate graphical representations of the atomic arrangement. For a more detailed control over the generated figures, you can consult the original ASE documentation. In this particular instance, we generate three distinct views of the BeO structure by applying different rotation angles. Specifically, we create a figure with three subplots, each displaying the molecule rotated along different axes: `('15x,15y,0z')`, `('0x,90y,0z')`, and `('0x,0y,0z')`." ] }, { "cell_type": "code", "execution_count": 3, "id": "736864d2-0a99-4631-9707-7f9b8d91fa7f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scm.plams import plot_molecule\n", "\n", "fig, ax_arr = plt.subplots(1, 3)\n", "ax_arr[0] = plot_molecule(mol, ax=ax_arr[0], rotation=('15x,15y,0z'))\n", "ax_arr[1] = plot_molecule(mol, ax=ax_arr[1], rotation=('0x,90y,0z'))\n", "ax_arr[2] = plot_molecule(mol, ax=ax_arr[2], rotation=('0x,0y,0z'))" ] }, { "cell_type": "markdown", "id": "5043639e-c8cf-468b-a4f1-07bb2c1c20f0", "metadata": {}, "source": [ "We now proceed to configure the Quantum Espresso calculation. Initially, a `Settings` object is created to store all the necessary parameters. To ensure the reliability of our vibrational spectrum, we specify a `GeometryOptimization` task, guaranteeing that our structure resides at a local energy minimum, thus avoiding contributions from imaginary frequencies. Subsequently, we explicitly request the calculation of `NormalModes` (which yields the IR spectrum) and the `Raman` spectrum within the Properties section (`s.input.ams.Properties`).\n", "\n", "For the electronic structure calculation, we set an energy cutoff (`ecutwfc`) of `25 Ry`. While this value is intentionally low for illustrative purposes and to speed up the calculation, it is important to note that for production calculations, a cutoff of at least `40 Ry` is generally recommended. The optimal cutoff value, however, depends on the specific system being investigated. We specify a Monkhorst–Pack grid with dimensions of `4 × 4 × 3` for sampling the first Brillouin zone, aiming for relatively uniform sampling in the three directions.\n", "\n", "Additionally, we specify the pseudopotentials required for the calculation. Due to Quantum Espresso’s limitation in supporting only Norm-Conserving (NC) and LDA-type pseudopotentials for Raman spectra calculations, which are not included in our standard pseudopotential families, we directly specify the pseudopotential files: `QE/Be.pz-mt_fhi.UPF` for beryllium and `QE/O.pz-mt_fhi.UPF` for oxygen. The pseudopotential filenames indicate the use of the Perdew-Zunger LDA functional (`pz`), the Martins-Troullier generation method (`mt`), and their origin from the FHI-aims library (`fhi`).\n", "\n", "Finally, we instantiate an `AMSJob` object, combining the defined `Settings` and the `Molecule` object, and we assign the name `\"BeO\"` to the job. This job object encapsulates all the necessary information for the Quantum Espresso calculation and is ready to be executed." ] }, { "cell_type": "code", "execution_count": 4, "id": "a33d28b9-6829-4e76-bd79-04fc0ab6bc40", "metadata": {}, "outputs": [], "source": [ "from scm.plams import Settings, AMSJob\n", "\n", "s = Settings()\n", "\n", "s.input.ams.Task = \"GeometryOptimization\"\n", "\n", "s.input.ams.Properties.NormalModes = \"Yes\"\n", "s.input.ams.Properties.Raman = \"Yes\"\n", "\n", "s.input.QuantumEspresso.System.ecutwfc = 25.0\n", "s.input.QuantumEspresso.K_Points._h = \"automatic\"\n", "s.input.QuantumEspresso.K_Points._1 = \"4 4 3 0 0 0\"\n", "s.input.QuantumEspresso.Pseudopotentials.Files = [\n", " Settings(Label=\"Be\", Path=\"QE/Be.pz-mt_fhi.UPF\"),\n", " Settings(Label=\"O\", Path=\"QE/O.pz-mt_fhi.UPF\")\n", "]\n", "\n", "job = AMSJob(settings=s, molecule=mol, name=\"BeO\")" ] }, { "cell_type": "markdown", "id": "38bc62f6-e18d-4f22-a9cc-842c6e6325f0", "metadata": {}, "source": [ "The `job.get_input()` function retrieves the content of the AMS input file generated during the previous setup. This enables users to inspect the input parameters before running the Quantum Espresso calculation. Reviewing the input file can be beneficial for advanced users who seek greater control over the calculation settings." ] }, { "cell_type": "code", "execution_count": 5, "id": "c88217c2-c2c7-472a-b7f0-320ba5de93c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Properties\n", " NormalModes Yes\n", " Raman Yes\n", "End\n", "\n", "Task GeometryOptimization\n", "\n", "System\n", " Atoms\n", " Be 0.0000000000 1.5555413800 0.0007009000\n", " Be 1.3471383600 0.7777706900 2.1887103500\n", " O 0.0000000000 1.5555413800 1.6512462200\n", " O 1.3471383600 0.7777706900 3.8392556700\n", " End\n", " Lattice\n", " 2.6942767100 0.0000000000 0.0000000000\n", " -1.3471383600 2.3333120800 0.0000000000\n", " 0.0000000000 0.0000000000 4.3760188900\n", " End\n", "End\n", "\n", "Engine QuantumEspresso\n", " K_Points automatic\n", " 4 4 3 0 0 0\n", " End\n", " Pseudopotentials\n", " Files\n", " Label Be\n", " Path QE/Be.pz-mt_fhi.UPF\n", " End\n", " Files\n", " Label O\n", " Path QE/O.pz-mt_fhi.UPF\n", " End\n", " End\n", " System\n", " ecutwfc 25.0\n", " End\n", "EndEngine\n", "\n", "\n" ] } ], "source": [ "print(job.get_input())" ] }, { "cell_type": "markdown", "id": "ca7b5394-5421-46ee-a112-33d9d1ce7ab6", "metadata": {}, "source": [ "We call the `run()` method on the previously defined `AMSJob` object to start the Quantum Espresso calculation. This starts the execution of the job, and once it is completed, the results are stored in the results object. The output displays various stages of the job's progress, including `STARTED`, `RUNNING`, `FINISHED`, and finally, `SUCCESSFUL`, which indicates that the calculation has been completed without any errors." ] }, { "cell_type": "code", "execution_count": 6, "id": "7cf13d39-88ae-4e3d-a9a6-59aacb571d15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[12.03|11:39:02] JOB BeO STARTED\n", "[12.03|11:39:02] JOB BeO RUNNING\n", "[12.03|11:39:35] JOB BeO FINISHED\n", "[12.03|11:39:35] JOB BeO SUCCESSFUL\n" ] } ], "source": [ "results = job.run()" ] }, { "cell_type": "markdown", "id": "44df9c43-d7f6-4878-a549-e5f30745f78b", "metadata": {}, "source": [ "The results object offers specialized functions for extracting calculated vibrational frequencies and intensities for both IR and Raman spectra. In the code below, we use the methods `get_frequencies()`, `get_ir_intensities()`, and `get_raman_intensities()` to obtain these values. Afterward, the code prints the frequencies (in cm⁻¹), IR intensities (in km/mol), and Raman intensities (in A⁴/mol) in a nice table with three columns." ] }, { "cell_type": "code", "execution_count": 7, "id": "7fe34a86-4fc7-41ea-9579-52d4e608be5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " freqs(cm-1) IR(km/mol) Raman(A^4/mol)\n", " 250.3 0.000 0.213\n", " 250.3 0.000 0.213\n", " 490.8 0.000 0.000\n", " 693.4 0.000 3.007\n", " 693.4 0.000 3.007\n", " 696.3 1043.853 13.503\n", " 741.3 990.970 4.991\n", " 741.3 990.970 4.991\n", " 830.8 0.000 0.000\n" ] } ], "source": [ "freqs = results.get_frequencies()\n", "ir_int = results.get_ir_intensities()\n", "raman_int = results.get_raman_intensities()\n", "\n", "print(f\"{'freqs(cm-1)':>15} {'IR(km/mol)':>15} {'Raman(A^4/mol)':>15}\")\n", "for freq, ir, raman in zip(freqs, ir_int, raman_int):\n", " print(f\"{freq:>15.1f} {ir:>15.3f} {raman:>15.3f}\")" ] }, { "cell_type": "markdown", "id": "ea6fa8db-8db4-423e-a4d3-c5d72d103f34", "metadata": {}, "source": [ "To visualize the IR spectrum, we use the `get_ir_spectrum` function, which applies broadening to the calculated absorptions in order to simulate the spectral lineshape observed in experiments. In this case, we use Lorentzian broadening with a width of 10 cm⁻¹ and focus on the spectral range from 200 to 1000 cm⁻¹. The `post_process=\"max_to_1\"` option normalizes the spectrum by setting the maximum intensity to 1.\n", "\n", "Then, we use matplotlib to visualize the results. We show the broadened IR spectrum as a curve (in blue) and small vertical bars (in red) to indicate the positions of the individual vibrational frequencies. Interestingly, the most prominent absorption in the spectrum results not from a single, high-intensity mode but rather from the overlap of two degenerate absorptions." ] }, { "cell_type": "code", "execution_count": 8, "id": "353095ec-5c94-4951-887f-a4939c44b4fe", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ir_data = results.get_ir_spectrum(broadening_type=\"lorentzian\", broadening_width=10, min_x=200, max_x=1000, post_process=\"max_to_1\")\n", "\n", "fig,ax = plt.subplots()\n", "ax.vlines(freqs, 0, len(freqs)*[ir_data[1].max()*0.05], color=\"r\" )\n", "ax.plot(*ir_data)\n", "ax.set_xlabel(\"Frequency (cm$^{-1}$)\")\n", "ax.set_ylabel(\"Intensity (arb.)\")\n", "ax.set_title(\"BeO IR Spectrum\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9d318c41-4f43-4078-ae2a-0eba4763c659", "metadata": {}, "source": [ "To visualize the Raman spectrum, we adopt a similar approach as with the IR spectrum, but using the `get_raman_spectrum` function. In contrast to the IR spectrum, the most prominent absorption in the Raman spectrum originates from a single, high-intensity mode rather than from the overlap of degenerate absorptions." ] }, { "cell_type": "code", "execution_count": 9, "id": "0dda8a36-0d97-408d-9301-0f7ba2467dd1", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "raman_data = results.get_raman_spectrum(broadening_type=\"lorentzian\", broadening_width=10, min_x=200, max_x=1000, post_process=\"max_to_1\")\n", "\n", "fig,ax = plt.subplots()\n", "ax.vlines(freqs, 0, len(freqs)*[raman_data[1].max()*0.05], color=\"r\")\n", "ax.plot(*raman_data)\n", "ax.set_xlabel(\"Frequency (cm$^{-1}$)\")\n", "ax.set_ylabel(\"Intensity (arb.)\")\n", "ax.set_title(\"BeO Raman Spectrum\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8ba03d0d-4f37-4cdc-bedf-ff2078af6084", "metadata": {}, "source": [ "The calculated IR and Raman spectra for beryllium oxide (BeO) can be compared to those available in [The Computational Raman Database](https://ramandb.oulu.fi/) (mpid: mp-2542). Our results are qualitatively consistent with those reported in this database. However, to reach more accurate results, it is essential to carefully optimize several parameters, such as using finer Brillouin zone sampling and a more appropriate energy cutoff." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }